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What Can You Do With Data?

A Question & Answer Period with Fred Schenkelberg and James Kovacevic on the what can be done with your data and analysis.

FAQData and the analyses that use the data can be tricky to manage at best, let along extremely difficult.  In this last post of the series on using the maintenance data you have, Fred and James will answer many of the common questions asked about data and the analyses.

How does one build a proper asset hierarchy? A hierarchy should be built to the recommendations in ISO 14224.  This will ensure all […]

By |2019-04-19T15:50:00-04:00July 18th, 2016|Defect Elimination, Reliability Engineering|Comments Off on What Can You Do With Data?

Next Steps in Your Data Analysis

4601202895_3a4bf4a848_mNothing keeps a statistician happy like a pile of data.  As seen in the previous articles, you can easily use the data you already have to conduct meaningful analysis.  This includes Weibull, Crow-AMSAA or a Mean Cumulative Failure analysis.

Digging into a well-managed dataset promises to reveal insights, trends, and patterns that will help improve the line, process, or plant.

Creating a plot or calculating summaries is pretty easy with today’s tools. Yet, are you doing the right analysis or are the various assumptions valid? One critical step in the data […]

By |2019-04-19T15:50:00-04:00July 11th, 2016|Defect Elimination, Reliability Engineering|Comments Off on Next Steps in Your Data Analysis

The Next Step in Your Failure Data

Improve your failure data to improve the speed and accuracy of your failure & reliability analysis.

24254846213_6b6950bbbd_mA few years into your reliability journey, you start to struggle to make the improvements you were able to when you first started.  Why is this?  You were able to systematically eliminate all of the low hanging fruit using the existing data in your CMMS.  But now you have to dig deeper to realize the improvements and that requires better data.

As Fred discussed in the previous post, A Mean Cumulative Failure Analysis can be another powerful tool […]

First Step in Analyzing Repairable Systems Data

Using the right plot enables your team to know what is working or need improvement.

MCFYour facility has data and maybe too much data. Using simple plotting may be the key to unlocking how well your maintenance program is performing.

Building on the concept of reliability growth modeling James Kovacevic described, a convenient way to quickly visualize your repairable system failure data is with a mean cumulative function (MCF) plot.

 

 

The Unacceptable and Common Approach

When confronted with a table of numbers our natural inclination is to find the average. It the average time to […]

By |2019-04-19T15:50:01-04:00June 27th, 2016|Defect Elimination, Reliability Engineering|Comments Off on First Step in Analyzing Repairable Systems Data

Quantify the Improvements (or Gaps) In Your Reliability

Using a Crow-AMSAA [Reliability Growth Analysis (RGA)] to Quantify Your Reliability Improvements (or Losses)

RGAImagine being able to predict the next time a failure will occur for a piece of equipment without a huge amount of work.  Wouldn’t it be nice to know the approximate point in time that a failure will occur on a critical piece of equipment?  It is possible, but I am not talking about using MTBF, as it is not a good measure (if you need to understand why, please visit http://www.NoMTBF.com).   What I am talking about is a […]

By |2019-04-19T15:50:01-04:00June 20th, 2016|Defect Elimination, Reliability Engineering|Comments Off on Quantify the Improvements (or Gaps) In Your Reliability